Stat110

Descriptive statistics, probability distributions, estimation, hypothesis testing, regression, analysis of count data, analysis of variance and experimental design, stat110. Sampling and design principles of techniques stat110 build on in the implementation of research studies.

Stat playlist on YouTube. Lecture 1: sample spaces, naive definition of probability, counting, sampling. Lecture 2: Bose-Einstein, story proofs, Vandermonde identity, axioms of probability. Lecture 3: birthday problem, properties of probability, inclusion-exclusion, matching problem. Lecture 5: law of total probability, conditional probability examples, conditional independence.

Stat110

A comprehensive introduction to probability as a language and toolbox for understanding statistics, science, risk, and randomness. The world is replete with randomness and uncertainty; probability and statistics extends logic into this realm. In this course, you will learn ideas and tools needed to understand the data and randomness that arise in many areas of science, engineering, economics, and finance. This course aims to provide a strong foundation for future study of statistical inference, stochastic processes, machine learning, randomized algorithms, econometrics, and other subjects where probability is needed. Prerequisites: All units require knowledge of algebra; Units require single variable calculus derivatives and integrals ; Unit 7 requires familiarity with matrices. No previous background in probability or statistics is required. This course has seven units of content, after a short introduction and orientation. A course map is available to help navigate the material. The course will end January 18, After the course ends, the content will remain available in archive mode. The discussion forums will close to new posts, but past posts will be read-only, and the staff will no longer be monitoring the course. The course team will send out occasional emails when new content is released, reminders closer to the course close, and for other occasional announcements. The messages will be archived under the course updates.

Back to top, stat110. HarvardX requires individuals who enroll in its courses on edX to abide by the terms of stat110 edX honor code. Stat playlist on YouTube.

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Descriptive statistics, probability distributions, estimation, hypothesis testing, regression, analysis of count data, analysis of variance and experimental design. Sampling and design principles of techniques to build on in the implementation of research studies. This is a paper in statistical methods for students from any of the sciences, including students studying biological sciences, social sciences or sport science, as well as those studying mathematics and statistics. The paper provides an introduction to the use of statistical methods for the description and analysis of data, use of computer software to carry out data analysis, and the interpretation of the results of statistical analyses for a range of research studies. Suitable for students of all disciplines with an interest in the quantitative analysis of data. There are no formal mathematical or statistical prerequisites for this paper, but students who have not done mathematics or statistics at NCEA Level 3 are encouraged to make use of the online and tutorial resources available as part of the paper. Four 1-hour lectures per week, plus cafeteria-style voluntary attendance tutorials each week for assistance with course material and exercises.

Stat110

Stat playlist on YouTube. Lecture 1: sample spaces, naive definition of probability, counting, sampling. Lecture 2: Bose-Einstein, story proofs, Vandermonde identity, axioms of probability. Lecture 3: birthday problem, properties of probability, inclusion-exclusion, matching problem. Lecture 5: law of total probability, conditional probability examples, conditional independence. Lecture 9: independence, Geometric, expected values, indicator r.

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Descriptive statistics, probability distributions, estimation, hypothesis testing, regression, analysis of count data, analysis of variance and experimental design. Summer School On campus Semester 1 On campus. HarvardX pursues the science of learning. Prerequisites: All units require knowledge of algebra; Units require single variable calculus derivatives and integrals ; Unit 7 requires familiarity with matrices. Lecture law of large numbers, central limit theorem. Lecture 5: law of total probability, conditional probability examples, conditional independence. Petersburg paradox Lecture sympathetic magic, Poisson distribution, Poisson approximation Lecture discrete vs. Stat playlist on YouTube. Suitable for students of all disciplines with an interest in the quantitative analysis of data. If you have any questions or concerns, please contact harvardx harvard. Lecture conditional expectation cont. Lecture transformations, LogNormal, convolutions, the probabilistic method. Lecture Markov chains cont. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs. HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code.

Learn probability, an essential language and set of tools for understanding data, randomness, and uncertainty. Probability and statistics help to bring logic to a world replete with randomness and uncertainty. This course will give you tools needed to understand data, science, philosophy, engineering, economics, and finance.

Copies of all lecture slides area available at the start of the course either in electronic or paper form. We encourage class participants to share their solutions and ideas for practice problems, whereas for homework problems, posting full solutions in the forums is not allowed. A comprehensive introduction to probability as a language and toolbox for understanding statistics, science, risk, and randomness. After the course ends, the content will remain available in archive mode. The practice problems are provided to help you practice with the concepts before tackling the homework problems. Skip to main content. Back to top. Petersburg paradox. Read our research statement to learn more. If you have collaborated with others in generating a correct solution to a problem, a good test to see if you were engaged in acceptable collaboration is to make sure that you are able to do the problem on your own. A course map is available to help navigate the material. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service.